Joint Effects of Context and User History for Predicting Online Conversation Re-entries

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2809–2818 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 2809 Joint Effects of Context and User History for Predicting Online Conversation Re-entries Xingshan Zeng1,2, Jing Li3∗, Lu Wang4, Kam-Fai Wong1,2 1The Chinese University of Hong Kong, Hong Kong 2MoE Key Laboratory of High Confidence Software Technologies, China 3Tencent AI Lab, Shenzhen, China 4Northeastern University, Boston, MA, United States 1,2{xszeng,kfwong}@se.cuhk.edu.hk 3ameliajli@tencent.com, 4luwang@ccs.neu.edu Abstract As the online world continues its exponen- tial growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage with each other online, we study a challenging problem of re-entry pre- diction foreseeing whether a user will come back to a conversation they once participated in. We hypothesize that both the context of the ongoing conversations and the users’ previous chatting history will affect their continued in- terests in future engagement. Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting his- tory jointly result in their re-entry behavior. We experiment with two large-scale datasets collected from Twitter and Reddit. Results show that our proposed framework with bi- attention achieves an F1 score of 61.1 on Twit- ter conversations, outperforming the state-of- the-art methods from previous work. 1 Introduction Interpersonal communication plays an important role in information exchange and idea sharing in our daily life. We are involved in a wide variety of dialogues every day, ranging from kitchen ta- ble conversations to online discussions, all help us make decisions, better understand important social issues, and form personal ideology. However, in- dividuals have limited attentions to engage in the massive amounts of online conversations. There thus exists a pressing need to develop automatic conversation management tools to keep track of the discussions one would like to keep engaging in. To meet such demand, we study the prob- lem of predicting online conversation re-entries, ∗ Jing Li is the corresponding author. …… H1: Is there literally no one on twitter who wants to talk about LET ME IN with me? :( H2: I think the change in overall tone was enough to let LMI stand on it's own. Love Giacchino's score too. H3: I think if i had seen LMI again before making my top ten it would have made the cut. Oh well. H4: it's not as bad as I remembered on the blu-ray. Looks like shit next to Avatar, but so does everything lol …… User History of U1 Conversation 1 Conversation 2 T1[U2]: Instead of focusing on when Oscars got it wrong... Let's talk about when the Oscars got it right… T2[U1]: The Hurt Locker, The Departed, NCFOM, LOTR, Schindler's List, Braveheart, Gladiator, The Godfather Part 1 & 2. ...... …… T1[U3]: Almost fell asleep in the first hour of INCEPTION. In the theatre. T2[U4]: lol do you …

Content truncated to 500 words